Summary of Expomamba: Exploiting Frequency Ssm Blocks For Efficient and Effective Image Enhancement, by Eashan Adhikarla et al.
ExpoMamba: Exploiting Frequency SSM Blocks for Efficient and Effective Image Enhancement
by Eashan Adhikarla, Kai Zhang, John Nicholson, Brian D. Davison
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents ExpoMamba, a novel architecture designed to enhance low-light images while ensuring computational efficiency. The authors address the limitations of existing state-of-the-art models in handling high-resolution images and slow inference times on edge devices. By integrating frequency state space components within a modified U-Net, ExpoMamba optimizes for mixed exposure challenges and achieves improved performance. Experimental results demonstrate a 2-3x speedup compared to traditional models with an inference time of 36.6 ms, and a PSNR improvement of approximately 15-20% over competing models, making it suitable for real-time image processing applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ExpoMamba is a new way to make low-light images brighter. Right now, computers can’t do this very well because the methods they use take too long or need too much computer power. The team behind ExpoMamba wanted to fix this problem by creating an architecture that’s fast and good at its job. They used ideas from something called the frequency state space and combined them with a type of neural network called U-Net. This new approach helps with mixed exposure, which is when some parts of an image are really bright and others are really dark. The team tested ExpoMamba and found it can make low-light images up to 2-3 times brighter than other methods, while also being fast enough for real-time use. |
Keywords
» Artificial intelligence » Inference » Neural network
